Teachers’ Linguistic Politeness in Classroom Interaction: A Pragmatic Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study aimed to uncover the different structures of linguistic politeness used in the utterances of the teachers in classroom interaction. More specifically, the analysis made use of House and Kasper’s (1981) Politeness Linguistic Expressions, Brown and Levinson’s (1987) Politeness Strategies, and Leech’s (1983) Politeness Maxims. Using observation and interview, several structures of linguistic politeness were unearthed. Firstly, the politeness linguistic expressions involved politeness markers, consultative devices, downtoners, committers, forewarning, hesitators, and agent avoider. Secondly, the politeness strategies involved positive politeness, negative politeness, off-record strategy, and bald-on record strategy. Lastly, the politeness maxims involved tact, approbation, modesty, and agreement maxim. Politeness is a non-value-laden linguistic phenomenon where it does not always mean what people in the here-and-now take it to mean, but there can always be a conventional ways of expressing so in a particular social interaction. The structures of linguistic politenesss do not always lead to conflict-avoidance, but they only contribute to the success of the effect of the expressions used. Hence, whatever may seem to have been considered as conventionally conventionalized or non-conventionalized politeness in a context, several factors must need to be considered for an expression to be a form of politeness strategy that performs supportive facework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it